Organizations must often analyze massive volumes of unstructured or raw text to gather information and determine actionable items. For example, in the pharmaceuticals industry, it is desirable for organizations to detect references to product and manufacturer sentiment within large text databases, reports, and the like to develop strategic plans. To achieve such a task, organizations often rely on natural language processing (NLP) systems. However, current NLP systems fail to provide NLP-as-a-service in which a user can supply raw text data to a service endpoint and receive back a predetermined set of outputs. Moreover, current NLP systems are extremely inefficient, utilizing overly complex and redundant software on top of hardware that is incapable of efficiently scaling to meet demand.